Evaluation of the clinical efficacy of a TW3-based fully automated bone age assessment system using deep neural networks.

PURPOSE: The aim of this study was to evaluate the clinical efficacy of a Tanner-Whitehouse 3 (TW3)-based fully automated bone age assessment system on hand-wrist radiographs of Korean children and adolescents. MATERIALS AND METHODS: Hand-wrist radiographs of 80 subjects (40 boys and 40 girls, 7-15 years of age) were collected. The clinical efficacy was evaluated by comparing the bone ages that were determined using the system with those from the reference standard produced by 2 oral and maxillofacial radiologists. Comparisons were conducted using the paired t-test and simple regression analysis. RESULTS: The bone ages estimated with this bone age assessment system were not significantly different from those obtained with the reference standard (P>0.05) and satisfied the equivalence criterion of 0.6 years within the 95% confidence interval (- 0.07 to 0.22), demonstrating excellent performance of the system. Similarly, in the comparisons of gender subgroups, no significant difference in bone age between the values produced by the system and the reference standard was observed (P>0.05 for both boys and girls). The determination coefficients obtained via regression analysis were 0.962, 0.945, and 0.952 for boys, girls, and overall, respectively (P=0.000); hence, the radiologist-determined bone ages and the system-determined bone ages were strongly correlated. CONCLUSION: This TW3-based system can be effectively used for bone age assessment based on hand-wrist radiographs of Korean children and adolescents.

Deep Learning Hybrid Method to Automatically Diagnose Periodontal Bone Loss and Stage Periodontitis.

We developed an automatic method for staging periodontitis on dental panoramic radiographs using the deep learning hybrid method. A novel hybrid framework was proposed to automatically detect and classify the periodontal bone loss of each individual tooth. The framework is a hybrid of deep learning architecture for detection and conventional CAD processing for classification. Deep learning was used to detect the radiographic bone level (or the CEJ level) as a simple structure for the whole jaw on panoramic radiographs. Next, the percentage rate analysis of the radiographic bone loss combined the tooth long-axis with the periodontal bone and CEJ levels. Using the percentage rate, we could automatically classify the periodontal bone loss. This classification was used for periodontitis staging according to the new criteria proposed at the 2017 World Workshop on the Classification of Periodontal and Peri-Implant Diseases and Conditions. The Pearson correlation coefficient of the automatic method with the diagnoses by radiologists was 0.73 overall for the whole jaw (p < 0.01), and the intraclass correlation value 0.91 overall for the whole jaw (p < 0.01). The novel hybrid framework that combined deep learning architecture and the conventional CAD approach demonstrated high accuracy and excellent reliability in the automatic diagnosis of periodontal bone loss and staging of periodontitis.

Steatocystoma multiplex: A case report of a rare entity.

Steatocystoma multiplex is an uncommon benign skin disease, which typically manifests as numerous intradermal cysts that can be scattered anywhere on the body. Although usually asymptomatic, it can be significantly disfiguring. One type of steatocystoma multiplex is known to be associated with the autosomal dominant inheritance of a mutation in the gene coding for keratin 17 (KRT17). In such cases, it is often concurrent with other developmental abnormalities of the ectoderm-derived tissues, such as the nails, hair, and teeth. To the best of our knowledge, few cases have been reported of steatocystoma multiplex of the oral and maxillofacial region. This report describes a case of steatocystoma multiplex of both sides of the neck and multiple dental anomalies, with a focus on its clinical, radiological, and histopathological characteristics, as well as the possibility that the patient exhibited the familial type of this condition.